Applications that enable access to online temporal media, such as videos and music, are increasingly prevalent on mobile devices. The temporal media may be stored online and streamed to the user by a site such as YOUTUBE™, or directly downloaded and played on the mobile device. Common features associated with temporal media are the ability of a user to view the media from a particular point, comment upon the media, bookmark the media, recommend the media to another user, and also rate the media. Comments, bookmarks, and ratings may be stored on the same site that hosts the temporal media, on yet another site, or locally on the mobile device.
Often, ratings are used to create a list of the “Most Popular” media on the site, allowing users quick access to those media. Popular rating systems include the “thumbs up” and “thumbs down” rating system utilized by social bookmarking Web sites such as DIGG™ and DELICIOUS™, and the “star system” utilized by video Web sites such as YOUTUBE™, NETFLIX™, and others. These rating systems require that the user make explicit input to the system and generally take the individual media (usually a single Web resource such as a URL) as a single entity for rating. Temporal media (such as movies and songs) are far more complex and can be taken as series of individual segments (e.g. scenes). Existing systems fail to systematically associate tacit user interests in the media, derived from micro-interactions with the media, a browser, or player, with individual media segments.
Thus, there is a need in the art for a system and method that implicitly identifies, or infers, attention hotspots within temporal media. The system and method also preferably aggregates attention hotspots from a number of users, and presents a user with suggested attention hotspots of interest for temporal media.